无需训练新模型，即可高效引导扩散模型解决各类逆问题。
Variational Control for Guidance in Diffusion Models
- 基于变分推断与控制理论，统一多种引导方法框架。
- 在多个线性与非线性逆问题上达到当前最佳性能。
- 适用于像素与潜在空间扩散模型，无需额外训练。
扩散模型生成样本质量优异，但现有引导方法常需额外训练或仅限特定任务。本文从变分推断与控制视角重新审视扩散模型的引导机制，提出扩散轨迹匹配（DTM），使预训练扩散轨迹可满足终端代价约束。DTM统一了广泛引导方法，并支持全新实例化。我们在此框架内提出新方法，在多个线性、非线性及盲逆问题上取得当前最优结果，无需额外模型训练，且不局限于像素或潜在空间扩散模型。代码将公开于 https://github.com/czi-ai/oc-guidance。
原文摘要 · Abstract (English)
Diffusion models exhibit excellent sample quality, but existing guidance methods often require additional model training or are limited to specific tasks. We revisit guidance in diffusion models from the perspective of variational inference and control, introducing Diffusion Trajectory Matching (DTM) that enables guiding pretrained diffusion trajectories to satisfy a terminal cost. DTM unifies a broad class of guidance methods and enables novel instantiations. We introduce a new method within this framework that achieves state-of-the-art results on several linear, non-linear, and blind inverse problems without requiring additional model training or specificity to pixel or latent space diffusion models. Our code will be available at https://github.com/czi-ai/oc-guidance
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